• 대한기계학회논문집A
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• 제28권12호
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• pp.1997-2003
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• 2004

This paper presents a new method and an associated device, capable of detecting protein presence and size from the shift of the mechanical stiffness changing points due to the presence and size of proteins in a nano-gap actuator. Compared to the conventional resonant detection method, the present nanomechanical stiffness detection method shows higher precision for protein detection. The present method also offers simple and inexpensive protein detection devices by removing labeling process and optical components. We design and fabricate the nanomechanical protein detector using an electrothermal actuator with a nano-gap. In the experimental study, we measure the stiffness changing points and their coordinate shift from the devices with and without target proteins. The fabricated device detects the protein presence and the protein size of 14.0$\pm$7.4nm based on the coordinate shift of stiffness changing points. We experimentally verify the protein presence and size detection capability of the nanomechanical protein detector for applications to high-precision biomolecule detection.

• International Journal of Aeronautical and Space Sciences
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• 제13권3호
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• pp.398-404
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• 2012

A fault detection process is necessary for high integrity systems like satellites, missiles and aircrafts. Especially, the satellite has to be expected to detect faults autonomously because it cannot be fixed by an expert in the space. Faults can cause critical errors to the entire system and the satellite does not have sufficient computation power to operate a large scale fault management system. Thus, a fault detection method, which has less computational burden, is required. In this paper, we proposed a modified PCA (Principal Component Analysis) as a powerful fault detection method of redundant IMU (Inertial Measurement Unit). The proposed method combines PCA with the parity space approach and it is much more efficient than the others. The proposed fault detection algorithm, modified PCA, is shown to outperform fault detection through a simulation example.

• Structural Engineering and Mechanics
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• 제54권2호
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• pp.221-237
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• 2015

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

• 한국태양에너지학회 논문집
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• 제26권1호
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• pp.91-98
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• 2006

Islanding phenomenon of utility-connected photovoltaic power conditioning systems(PV PCS) can cause a variety of problems and must be prevented. If the real and reactive power supplied by PV PCS are closely matched to those of load, islanding detection by passive methods becomes difficult. The active frequency drift(AFD) method, called the frequency bias method, enables islanding detection by forcing the frequency of the voltage in the islanding to drift up or down. In this paper, non-detection zone(NDZ) of AFD is analyzed for the islanding detection method of utility-connected PV PCS by the simulation software tool PSIM.

• 한국멀티미디어학회논문지
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• 제24권9호
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• pp.1242-1250
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• 2021

Detection of outliers deviating normal data distribution in high dimensional data is an important technique in many application areas. In this paper, a distance-based outlier detection method using landmarks in high dimensional data is proposed. Given normal training data, the k-means clustering method is applied for the training data in order to extract the centers of the clusters as landmarks which represent normal data distribution. For a test data sample, the distance to the nearest landmark gives the outlier score. In the experiments using high dimensional data such as images and documents, it was shown that the proposed method based on the landmarks of one-tenth of training data can give the comparable outlier detection performance while reducing the time complexity greatly in the testing stage.

• International Journal of Internet, Broadcasting and Communication
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• 제13권2호
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• pp.187-194
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• 2021

This paper presents a margin-based face liveness detection method with behavioral confirmation to prevent spoofing attacks using deep learning techniques. The proposed method provides a possibility to prevent biometric person authentication systems from replay and printed spoofing attacks. For this work, a set of real face images and fake face images was collected and a face liveness detection model is trained on the constructed dataset. Traditional face liveness detection methods exploit the face image covering only the face regions of the human head image. However, outside of this region of interest (ROI) might include useful features such as phone edges and fingers. The proposed face liveness detection method was experimentally tested on the author's own dataset. Collected databases are trained and experimental results show that the trained model distinguishes real face images and fake images correctly.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.1-6
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• 2022

Scene change detection is an important topic because of the wide and growing range of its applications. Streaming services from many providers are increasing their capacity which causes the industry growth. The method for the scene change detection is described here and compared with the State-of-the-Art methods over the Open Video Scene Detection (OVSD) - an open dataset of Creative Commons licensed videos freely available for download and use to evaluate video scene detection algorithms. The proposed method is based on scene analysis using threshold values and smooth scene changes. A comparison of the presented method was conducted in this research. The obtained results demonstrated the high efficiency of the scene cut localization method proposed by authors, because its efficiency measured in terms of precision, recall, accuracy, and F-metrics score exceeds the best previously known results.

• The Plant Pathology Journal
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• 제39권1호
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• pp.141-148
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• 2023

Black shoot blight disease caused by Erwinia pyrifoliae has serious impacts on quality and yield in pear production in Korea; therefore, rapid and accurate methods for its detection are needed. However, traditional detection methods require a great deal of time and fail to achieve absolute quantification. In the present study, we developed a droplet digital polymerase chain reaction (ddPCR) method for the detection and absolute quantification of E. pyrifoliae using a pair of species-specific primers. The detection range was 10³-10⁷ copies/ml (DNA templates) and cfu/ml (cell culture templates). This new method exhibited good linearity and repeatability and was validated by absolute quantification of E. pyrifoliae DNA copies from samples of artificially inoculated immature pear fruits. Here, we present the first study of ddPCR assay for the detection and quantification of E. pyrifoliae. This method has potential applications in epidemiology and for the early prediction of black shoot blight outbreaks.

• Structural Engineering and Mechanics
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• 제83권5호
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• pp.593-607
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• 2022

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권4호
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• pp.170-177
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• 2016

문자 인코딩은 문서를 컴퓨터에서 이용할 수 있도록 문자 코드 테이블을 이용하여 이진화하는 방법이다. 이진화된 문서를 읽기 위해서는, 문서에 적용된 문자 코드를 이용하여 문자 인코딩을 알아내야 한다. 본 논문에서는 문서의 문자 인코딩을 자동으로 판별하는 방법을 제시한다. 제안하는 방법은 이스케이프 문자를 이용한 판별법, 문서에 나타난 코드 값 범위 판별법, 문서에 나타난 코드 값의 특징 판별법, 각 언어별 자주 사용하는 단어를 이용한 판별법과 같은 여러 단계를 걸쳐 문서에 적용된 문자 인코딩을 판별한다. 자주 사용하는 단어를 이용한 방법은 문서를 언어별로 분류하여 문자 인코딩을 판별하기 때문에, 다국어 문서에서 기존의 방법보다 높은 문자 인코딩 인식률을 보인다. 주로 표현하는 언어의 비중이 20% 미만일 경우, 기존의 방법은 약 50%의 문자 인코딩 인식률을 보였으나, 제안하는 방법은 문자 인코딩에서 표현하는 언어의 비중과는 상관없이 96% 이상의 문자 인코딩 인식률을 보였다.